Traffic counting refers to counting the number of movable objects moving in certain directions in a given period of time. For example, the objects may be people or vehicles. Traffic counting has various applications, such as determining the number of people moving into and/or out of a building, the number of passengers entering and/or existing a bus or train, the number of vehicles moving into and/or out of a parking lot, the number of pedestrians/vehicles moving in a given direction, etc.
In recent years, traffic counting has gained tremendous attention in many industry and service sectors. For example, consumer businesses (e.g., retail stores, supermarkets, etc.) are becoming increasingly aware of the importance of monitoring the number of visitor to their establishments. Customer traffic data may be analyzed to better organize staff shifts, manage inventory, evaluate sales performance, conduct marketing research, etc. The trend for improved insights to patronage can also be seen at other locations, such as, exhibition halls, sports and gym facilities, and public institutions such as libraries, universities, and hospitals.
Also for example, traffic counting may be used for security or safety reasons. In the case of an evacuation, it is essential to know how many people are inside a building at any given time. A fire department, for instance, needs the traffic data to understand the activities in a building and manage fire exits of the building, e.g., adding or removing fire exits, altering the size of the fire exits, etc.
For yet another example, in the current efforts to build “smart cities,” decision makers are increasingly relying on the traffic counting data to make informed decisions on regulating public transportation, distributing public resources, etc.
Conventionally, traffic counting is manually performed by human workers. However, humans are error prone, tire easily, and cannot uninterruptedly monitor the traffic. For example, a human worker may miscount in a crowded place. Moreover, human labor is expensive and thus the traffic counting usually has to be performed in an ad hoc manner. The data range may only cover a short time span such as several hours or several days, and cannot offer insights into the long-term trend of the traffic. Therefore, the manually obtained data are usually lack of accuracy and comprehensiveness. Devices and methods are needed for automatically performing traffic counting.
The disclosed methods and systems address one or more of the problems listed above.